Overview

Dataset statistics

Number of variables11
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory43.1 KiB
Average record size in memory88.3 B

Variable types

NUM11

Reproduction

Analysis started2020-08-25 00:27:48.668058
Analysis finished2020-08-25 00:28:07.395049
Duration18.73 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

oz5 is highly correlated with oz3High correlation
oz3 is highly correlated with oz5High correlation
oz1 has unique values Unique
oz2 has unique values Unique
oz3 has unique values Unique
oz4 has unique values Unique
oz5 has unique values Unique
oz6 has unique values Unique
oz7 has unique values Unique
oz8 has unique values Unique
oz9 has unique values Unique
oz10 has unique values Unique
target has unique values Unique

Variables

oz1
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2367963790893555e-09
Minimum-2.182015895843506
Maximum2.3030824661254883
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:28:07.440694image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.182015896
5-th percentile-1.587574285
Q1-0.7330114841
median-0.09320949763
Q30.7945423424
95-th percentile1.612203246
Maximum2.303082466
Range4.485098362
Interquartile range (IQR)1.527553827

Descriptive statistics

Standard deviation1.000000003
Coefficient of variation (CV)808540532.3
Kurtosis-0.8583043058
Mean1.236796379e-09
Median Absolute Deviation (MAD)0.7666672766
Skewness0.04198504475
Sum6.183981895e-07
Variance1.000000005
2020-08-25T00:28:07.542942image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.451170623310.2%
 
-1.07492923710.2%
 
-0.372877001810.2%
 
0.336108416310.2%
 
-1.0253763210.2%
 
0.631203949510.2%
 
-0.232508227210.2%
 
1.54366195210.2%
 
-0.0230821706410.2%
 
0.973005473610.2%
 
-1.11782562710.2%
 
-1.11984288710.2%
 
-0.577501058610.2%
 
1.61399042610.2%
 
-0.485173940710.2%
 
-1.18040692810.2%
 
1.61210918410.2%
 
0.10441522310.2%
 
0.223255634310.2%
 
-0.838255226610.2%
 
0.502318799510.2%
 
0.636111676710.2%
 
1.50209057310.2%
 
0.246976882210.2%
 
-0.346868842810.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-2.18201589610.2%
 
-2.07822823510.2%
 
-2.07626700410.2%
 
-2.03510403610.2%
 
-1.97636675810.2%
 
-1.95219862510.2%
 
-1.93162083610.2%
 
-1.92415344710.2%
 
-1.8999676710.2%
 
-1.88883459610.2%
 
ValueCountFrequency (%) 
2.30308246610.2%
 
2.10964536710.2%
 
2.09326291110.2%
 
2.00192236910.2%
 
1.97772121410.2%
 
1.96221971510.2%
 
1.95847594710.2%
 
1.94439351610.2%
 
1.87504017410.2%
 
1.86608874810.2%
 

oz2
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.338821887969971e-10
Minimum-1.6521769762039185
Maximum1.6996958255767822
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:28:07.655108image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.652176976
5-th percentile-1.533615953
Q1-0.8653020263
median-0.07433986291
Q30.8689539433
95-th percentile1.580094606
Maximum1.699695826
Range3.351872802
Interquartile range (IQR)1.73425597

Descriptive statistics

Standard deviation1.000000005
Coefficient of variation (CV)-1362616535
Kurtosis-1.214606377
Mean-7.338821888e-10
Median Absolute Deviation (MAD)0.8728077412
Skewness0.04495610459
Sum-3.669410944e-07
Variance1.00000001
2020-08-25T00:28:07.760896image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.32985234310.2%
 
-1.17647886310.2%
 
-0.524742364910.2%
 
-0.674158155910.2%
 
-0.222817748810.2%
 
1.40105450210.2%
 
0.351730138110.2%
 
0.916655778910.2%
 
0.614596307310.2%
 
-0.946630179910.2%
 
0.375759601610.2%
 
-0.0729188919110.2%
 
1.39912784110.2%
 
-0.873321652410.2%
 
0.62644189610.2%
 
-0.0265393406210.2%
 
0.155610948810.2%
 
0.0362769104510.2%
 
-0.15194423510.2%
 
-0.550161004110.2%
 
-0.915395736710.2%
 
-0.24252158410.2%
 
0.833367705310.2%
 
0.889032542710.2%
 
-1.53978729210.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.65217697610.2%
 
-1.64365160510.2%
 
-1.63444054110.2%
 
-1.62147057110.2%
 
-1.62076640110.2%
 
-1.61302864610.2%
 
-1.61115825210.2%
 
-1.6079653510.2%
 
-1.60756623710.2%
 
-1.60312509510.2%
 
ValueCountFrequency (%) 
1.69969582610.2%
 
1.69754862810.2%
 
1.69731497810.2%
 
1.69100773310.2%
 
1.68936383710.2%
 
1.68404507610.2%
 
1.68290138210.2%
 
1.66558325310.2%
 
1.66163146510.2%
 
1.65492594210.2%
 

oz3
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.497195964679122e-09
Minimum-2.0540246963500977
Maximum3.383193016052246
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:28:07.875890image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.054024696
5-th percentile-1.316987044
Q1-0.7316944897
median-0.153934285
Q30.5904056579
95-th percentile1.955146444
Maximum3.383193016
Range5.437217712
Interquartile range (IQR)1.322100148

Descriptive statistics

Standard deviation0.9999999994
Coefficient of variation (CV)-400449149.2
Kurtosis0.3223191488
Mean-2.497195965e-09
Median Absolute Deviation (MAD)0.6461506635
Skewness0.7760453925
Sum-1.248597982e-06
Variance0.9999999989
2020-08-25T00:28:07.977789image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.242431491610.2%
 
0.267885118710.2%
 
-0.288730442510.2%
 
0.317540317810.2%
 
1.28774106510.2%
 
-0.530591189910.2%
 
0.81184238210.2%
 
-0.0335282832410.2%
 
-0.635087907310.2%
 
0.479653686310.2%
 
0.0924477353710.2%
 
1.91081047110.2%
 
0.575523197710.2%
 
1.17253220110.2%
 
-0.591611623810.2%
 
-1.15106201210.2%
 
-0.682011485110.2%
 
-0.815767824610.2%
 
-0.231775179510.2%
 
-1.00320935210.2%
 
-0.202480062810.2%
 
-0.352226555310.2%
 
-0.661350071410.2%
 
-0.403496742210.2%
 
0.342463403910.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-2.05402469610.2%
 
-1.78701329210.2%
 
-1.74396336110.2%
 
-1.63676559910.2%
 
-1.62204301410.2%
 
-1.61723744910.2%
 
-1.60908031510.2%
 
-1.59431505210.2%
 
-1.57992899410.2%
 
-1.57895600810.2%
 
ValueCountFrequency (%) 
3.38319301610.2%
 
3.09228324910.2%
 
2.95615196210.2%
 
2.93629646310.2%
 
2.86939716310.2%
 
2.85361862210.2%
 
2.8011596210.2%
 
2.7913992410.2%
 
2.63898706410.2%
 
2.51433920910.2%
 

oz4
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.3469252735376358e-10
Minimum-1.8704309463500977
Maximum1.7110344171524048
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:28:08.085021image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.870430946
5-th percentile-1.636066639
Q1-0.8032520562
median0.05340874009
Q30.8717503995
95-th percentile1.506763268
Maximum1.711034417
Range3.581465364
Interquartile range (IQR)1.675002456

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)-7424316847
Kurtosis-1.116656081
Mean-1.346925274e-10
Median Absolute Deviation (MAD)0.8431550581
Skewness-0.05837882486
Sum-6.734626368e-08
Variance1
2020-08-25T00:28:08.185866image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.18749928510.2%
 
-0.99054735910.2%
 
-0.683044493210.2%
 
-0.323877036610.2%
 
-0.328034967210.2%
 
1.00136280110.2%
 
-1.81113505410.2%
 
-1.34239304110.2%
 
0.104030616610.2%
 
-0.636417806110.2%
 
-0.729795396310.2%
 
0.758116424110.2%
 
1.06334173710.2%
 
1.23108255910.2%
 
-1.42639911210.2%
 
-1.00453448310.2%
 
1.46552395810.2%
 
-1.66860032110.2%
 
-0.973315417810.2%
 
1.61185371910.2%
 
-1.63540685210.2%
 
1.69009637810.2%
 
-0.530599355710.2%
 
0.346843570510.2%
 
-0.366375267510.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.87043094610.2%
 
-1.86869263610.2%
 
-1.84969270210.2%
 
-1.83951866610.2%
 
-1.83378052710.2%
 
-1.82443451910.2%
 
-1.82281434510.2%
 
-1.81595838110.2%
 
-1.81208050310.2%
 
-1.81113505410.2%
 
ValueCountFrequency (%) 
1.71103441710.2%
 
1.70888304710.2%
 
1.70802748210.2%
 
1.70031797910.2%
 
1.69817410.2%
 
1.69768500310.2%
 
1.69009637810.2%
 
1.68446719610.2%
 
1.68054425710.2%
 
1.68024921410.2%
 

oz5
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.6508856823804764e-09
Minimum-1.4666876792907717
Maximum4.994360446929932
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:28:08.302191image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.466687679
5-th percentile-1.047098398
Q1-0.6832123846
median-0.3202890158
Q30.4444674999
95-th percentile2.17245034
Maximum4.994360447
Range6.461048126
Interquartile range (IQR)1.127679884

Descriptive statistics

Standard deviation0.9999999978
Coefficient of variation (CV)-605735459.7
Kurtosis2.503528605
Mean-1.650885682e-09
Median Absolute Deviation (MAD)0.4690162241
Skewness1.526035605
Sum-8.254428412e-07
Variance0.9999999956
2020-08-25T00:28:08.414781image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.221191868210.2%
 
2.34892344510.2%
 
0.0526203960210.2%
 
-0.0512267611910.2%
 
0.465479731610.2%
 
-0.679006874610.2%
 
-0.00278971274410.2%
 
-0.659481048610.2%
 
-0.573544681110.2%
 
-1.17834126910.2%
 
3.46764278410.2%
 
0.347322970610.2%
 
1.60609090310.2%
 
1.50257873510.2%
 
0.0685207173210.2%
 
0.401752740110.2%
 
1.02707624410.2%
 
-0.195880413110.2%
 
-0.189654663210.2%
 
0.460610002310.2%
 
2.54404425610.2%
 
-0.936851978310.2%
 
-0.458384275410.2%
 
0.794280707810.2%
 
-0.847019195610.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.46668767910.2%
 
-1.3709971910.2%
 
-1.35359275310.2%
 
-1.31948673710.2%
 
-1.30984747410.2%
 
-1.28675234310.2%
 
-1.28420424510.2%
 
-1.17834126910.2%
 
-1.17325401310.2%
 
-1.16243517410.2%
 
ValueCountFrequency (%) 
4.99436044710.2%
 
3.65761756910.2%
 
3.46968841610.2%
 
3.46764278410.2%
 
3.37120842910.2%
 
3.31432390210.2%
 
3.1029977810.2%
 
3.02926564210.2%
 
2.97942352310.2%
 
2.95688605310.2%
 

oz6
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9.264331310987473e-10
Minimum-2.0947365760803223
Maximum3.898322582244873
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:28:08.529901image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.094736576
5-th percentile-1.360453838
Q1-0.7266458571
median-0.1761063635
Q30.6539099514
95-th percentile1.873107588
Maximum3.898322582
Range5.993059158
Interquartile range (IQR)1.380555809

Descriptive statistics

Standard deviation1.000000004
Coefficient of variation (CV)-1079408724
Kurtosis0.1366666125
Mean-9.264331311e-10
Median Absolute Deviation (MAD)0.6461686194
Skewness0.6792138542
Sum-4.632165655e-07
Variance1.000000007
2020-08-25T00:28:08.643044image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.266111135510.2%
 
-1.10217404410.2%
 
-0.72514879710.2%
 
-1.21930742310.2%
 
0.324360042810.2%
 
1.87241387410.2%
 
-1.13339579110.2%
 
1.13144540810.2%
 
0.163744315510.2%
 
0.919241011110.2%
 
1.28184521210.2%
 
0.898736298110.2%
 
0.380033403610.2%
 
-0.547178685710.2%
 
0.0787734538310.2%
 
-0.660463631210.2%
 
-0.393720328810.2%
 
-1.08069467510.2%
 
-0.183915317110.2%
 
-0.613591790210.2%
 
0.263339936710.2%
 
0.469699740410.2%
 
-0.504222810310.2%
 
-0.560664176910.2%
 
1.01431918110.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-2.09473657610.2%
 
-1.75464117510.2%
 
-1.73432171310.2%
 
-1.68902277910.2%
 
-1.68780982510.2%
 
-1.680435310.2%
 
-1.67460274710.2%
 
-1.59207129510.2%
 
-1.5386147510.2%
 
-1.53833007810.2%
 
ValueCountFrequency (%) 
3.89832258210.2%
 
3.03847193710.2%
 
2.77914404910.2%
 
2.68684434910.2%
 
2.67061114310.2%
 
2.60025191310.2%
 
2.59628844310.2%
 
2.54429316510.2%
 
2.44438242910.2%
 
2.34756827410.2%
 

oz7
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.227032721042633e-10
Minimum-1.813788890838623
Maximum1.6325095891952517
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:28:08.916916image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.813788891
5-th percentile-1.645078737
Q1-0.8521927595
median0.1393411905
Q30.8676157892
95-th percentile1.443181163
Maximum1.632509589
Range3.44629848
Interquartile range (IQR)1.719808549

Descriptive statistics

Standard deviation0.9999999996
Coefficient of variation (CV)-3098822002
Kurtosis-1.202313505
Mean-3.227032721e-10
Median Absolute Deviation (MAD)0.8639413863
Skewness-0.149652907
Sum-1.613516361e-07
Variance0.9999999992
2020-08-25T00:28:09.021184image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.16210663310.2%
 
0.238852590310.2%
 
0.0100602218910.2%
 
-1.45763635610.2%
 
1.30725288410.2%
 
-0.998357355610.2%
 
0.0311315413610.2%
 
0.859691381510.2%
 
-0.724992036810.2%
 
-0.873366355910.2%
 
0.36930179610.2%
 
-1.63541686510.2%
 
0.118815548710.2%
 
0.910803139210.2%
 
-1.32292759410.2%
 
1.61785674110.2%
 
-0.283873349410.2%
 
0.621429860610.2%
 
-0.305515408510.2%
 
1.35869848710.2%
 
-0.522799849510.2%
 
-0.876040697110.2%
 
-0.284789562210.2%
 
-0.591164767710.2%
 
0.180820584310.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.81378889110.2%
 
-1.81357574510.2%
 
-1.79313886210.2%
 
-1.78312408910.2%
 
-1.77272403210.2%
 
-1.76817846310.2%
 
-1.75173544910.2%
 
-1.7459882510.2%
 
-1.7380762110.2%
 
-1.73756420610.2%
 
ValueCountFrequency (%) 
1.63250958910.2%
 
1.62949359410.2%
 
1.62251234110.2%
 
1.61785674110.2%
 
1.61224174510.2%
 
1.59420561810.2%
 
1.57728159410.2%
 
1.56064093110.2%
 
1.55576050310.2%
 
1.54402685210.2%
 

oz8
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.846392691135407e-10
Minimum-1.7033414840698242
Maximum1.7674155235290527
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:28:09.136915image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.703341484
5-th percentile-1.506660914
Q1-0.879682526
median-0.03123112395
Q30.8855373859
95-th percentile1.557325715
Maximum1.767415524
Range3.470757008
Interquartile range (IQR)1.765219912

Descriptive statistics

Standard deviation1.000000001
Coefficient of variation (CV)-1274471009
Kurtosis-1.206204486
Mean-7.846392691e-10
Median Absolute Deviation (MAD)0.878462404
Skewness0.05881849628
Sum-3.923196346e-07
Variance1.000000002
2020-08-25T00:28:09.240891image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.04296350510.2%
 
0.954443514310.2%
 
1.74434816810.2%
 
-0.705864429510.2%
 
1.34440004810.2%
 
1.54166853410.2%
 
-1.07682776510.2%
 
0.0373940281610.2%
 
1.06315994310.2%
 
-1.02214896710.2%
 
-1.3860509410.2%
 
0.787444889510.2%
 
1.53387510810.2%
 
0.823579490210.2%
 
0.911472916610.2%
 
1.63740420310.2%
 
-1.35229814110.2%
 
-0.476735055410.2%
 
1.35811245410.2%
 
0.809917151910.2%
 
0.802105188410.2%
 
-1.13092875510.2%
 
-0.792343437710.2%
 
1.03390824810.2%
 
-0.0837844684710.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.70334148410.2%
 
-1.69341659510.2%
 
-1.68724131610.2%
 
-1.68552315210.2%
 
-1.68532991410.2%
 
-1.67861163610.2%
 
-1.67852318310.2%
 
-1.66412675410.2%
 
-1.66373777410.2%
 
-1.65398693110.2%
 
ValueCountFrequency (%) 
1.76741552410.2%
 
1.76118695710.2%
 
1.75199866310.2%
 
1.75135517110.2%
 
1.7508909710.2%
 
1.74434816810.2%
 
1.73863029510.2%
 
1.72558784510.2%
 
1.72383427610.2%
 
1.72226715110.2%
 

oz9
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0710209608078003e-11
Minimum-1.731812596321106
Maximum1.6978249549865725
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:28:09.356047image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.731812596
5-th percentile-1.566685426
Q1-0.898710832
median-0.003703104216
Q30.8872013986
95-th percentile1.516347241
Maximum1.697824955
Range3.429637551
Interquartile range (IQR)1.785912231

Descriptive statistics

Standard deviation0.9999999977
Coefficient of variation (CV)9.336885404e+10
Kurtosis-1.209643193
Mean1.071020961e-11
Median Absolute Deviation (MAD)0.8919643032
Skewness-0.03973658532
Sum5.355104804e-09
Variance0.9999999954
2020-08-25T00:28:09.460914image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.27538800210.2%
 
-1.66209745410.2%
 
-0.433264821810.2%
 
0.721022903910.2%
 
-1.61587727110.2%
 
-1.12033915510.2%
 
-1.20572936510.2%
 
-0.770834624810.2%
 
1.51628172410.2%
 
0.0662034004910.2%
 
1.26041710410.2%
 
1.09246444710.2%
 
-0.228322431410.2%
 
1.13775014910.2%
 
0.536472499410.2%
 
-1.29365634910.2%
 
-1.5671631110.2%
 
0.887065291410.2%
 
-0.0952582806310.2%
 
0.115434497610.2%
 
1.17062914410.2%
 
-0.837270200310.2%
 
0.521841645210.2%
 
-1.05665552610.2%
 
-1.49681699310.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.73181259610.2%
 
-1.72788119310.2%
 
-1.71693801910.2%
 
-1.7160499110.2%
 
-1.71244370910.2%
 
-1.71088314110.2%
 
-1.69188284910.2%
 
-1.66653871510.2%
 
-1.66209745410.2%
 
-1.65646386110.2%
 
ValueCountFrequency (%) 
1.69782495510.2%
 
1.69416952110.2%
 
1.69353234810.2%
 
1.69324040410.2%
 
1.69159734210.2%
 
1.69073331410.2%
 
1.690617810.2%
 
1.67441618410.2%
 
1.66564524210.2%
 
1.66180825210.2%
 

oz10
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.838251233100891e-10
Minimum-1.7813730239868164
Maximum1.7427693605422974
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:28:09.576165image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.781373024
5-th percentile-1.579803324
Q1-0.8659646809
median0.05214556307
Q30.8416701704
95-th percentile1.547916901
Maximum1.742769361
Range3.524142385
Interquartile range (IQR)1.707634851

Descriptive statistics

Standard deviation0.9999999998
Coefficient of variation (CV)1131445547
Kurtosis-1.193168769
Mean8.838251233e-10
Median Absolute Deviation (MAD)0.8464054242
Skewness-0.05578470199
Sum4.419125617e-07
Variance0.9999999995
2020-08-25T00:28:09.680236image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.18359112710.2%
 
0.999125182610.2%
 
1.74276936110.2%
 
-1.13418114210.2%
 
-1.4017361410.2%
 
-0.671193897710.2%
 
-1.21543645910.2%
 
-1.47551834610.2%
 
1.67176353910.2%
 
-0.158767402210.2%
 
0.990539431610.2%
 
-0.389801174410.2%
 
-0.953561246410.2%
 
-1.60608339310.2%
 
0.726235508910.2%
 
1.1490662110.2%
 
-0.859041988810.2%
 
-1.22597181810.2%
 
-1.39126384310.2%
 
1.55318081410.2%
 
-0.519252777110.2%
 
-1.21354091210.2%
 
-0.997992217510.2%
 
1.35762846510.2%
 
-0.0718188136810.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-1.78137302410.2%
 
-1.77853262410.2%
 
-1.75296878810.2%
 
-1.74799585310.2%
 
-1.73831605910.2%
 
-1.72885477510.2%
 
-1.71896684210.2%
 
-1.70902073410.2%
 
-1.70263636110.2%
 
-1.69931876710.2%
 
ValueCountFrequency (%) 
1.74276936110.2%
 
1.73399877510.2%
 
1.73123085510.2%
 
1.70727622510.2%
 
1.70716464510.2%
 
1.70671796810.2%
 
1.7013851410.2%
 
1.68942654110.2%
 
1.67692768610.2%
 
1.67176353910.2%
 

target
Real number (ℝ)

UNIQUE

Distinct count500
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8696300330134363e-10
Minimum-2.5621426105499268
Maximum3.17509388923645
Zeros0
Zeros (%)0.0%
Memory size4.0 KiB
2020-08-25T00:28:09.796039image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.562142611
5-th percentile-1.87171452
Q1-0.605491221
median0.1281313114
Q30.7087292224
95-th percentile1.31906743
Maximum3.175093889
Range5.7372365
Interquartile range (IQR)1.314220443

Descriptive statistics

Standard deviation1.000000001
Coefficient of variation (CV)5348651785
Kurtosis0.06206761629
Mean1.869630033e-10
Median Absolute Deviation (MAD)0.6339848936
Skewness-0.2715675597
Sum9.348150165e-08
Variance1.000000003
2020-08-25T00:28:09.898540image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-2.03515577310.2%
 
0.640972375910.2%
 
1.3483090410.2%
 
0.174642920510.2%
 
0.705527365210.2%
 
-0.156088948210.2%
 
-0.243003055510.2%
 
0.706389546410.2%
 
0.460129320610.2%
 
-1.10239398510.2%
 
-0.387865155910.2%
 
0.54037970310.2%
 
-0.0684603154710.2%
 
-1.70453357710.2%
 
-1.76240944910.2%
 
0.393730074210.2%
 
-0.875700473810.2%
 
-0.528671741510.2%
 
-0.633167624510.2%
 
-0.1150677810.2%
 
0.668326556710.2%
 
0.690857291210.2%
 
0.0156153859610.2%
 
1.99877381310.2%
 
0.842161595810.2%
 
Other values (475)47595.0%
 
ValueCountFrequency (%) 
-2.56214261110.2%
 
-2.49111938510.2%
 
-2.41486525510.2%
 
-2.39954161610.2%
 
-2.35073447210.2%
 
-2.32773613910.2%
 
-2.28147959710.2%
 
-2.24897551510.2%
 
-2.24266815210.2%
 
-2.20308613810.2%
 
ValueCountFrequency (%) 
3.17509388910.2%
 
2.90995764710.2%
 
2.83544135110.2%
 
2.82128524810.2%
 
2.56125259410.2%
 
2.20427036310.2%
 
2.20105576510.2%
 
2.05337119110.2%
 
2.0308806910.2%
 
2.02772116710.2%
 

Interactions

2020-08-25T00:27:49.155385image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:49.276548image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:49.408263image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:49.532436image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:49.670531image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:49.803418image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:49.930938image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:50.063224image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:50.196728image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:50.327985image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:50.460428image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:50.588060image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:50.724150image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:50.865245image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:51.003607image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:51.148393image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:51.287215image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:51.427825image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:51.569452image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:51.710536image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:51.850611image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:51.991175image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:52.128331image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:52.449239image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:52.584275image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:52.722055image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:52.860843image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:52.993991image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:53.126467image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:53.261308image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:53.398417image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:53.535473image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:53.674109image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:53.807974image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:53.946741image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:54.113938image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:54.259830image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:54.410998image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:54.555646image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:54.700681image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:54.850371image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:54.998800image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:55.147213image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:55.298249image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:55.442960image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:55.573088image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:55.712881image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:55.860263image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:56.005793image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:56.144606image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:56.287166image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:56.432554image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:56.779140image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:56.920237image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:57.066183image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:57.224170image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:57.403563image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:57.590706image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:57.736283image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:57.893231image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:58.034377image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:58.171769image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:58.311997image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:58.451963image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:58.594636image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:58.749570image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:58.893010image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:59.025028image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:59.166891image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:59.302946image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:59.450070image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:59.595596image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:59.735221image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:27:59.876253image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:00.017122image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:00.158200image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:00.311790image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:00.453683image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:00.585699image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:00.725689image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:00.867799image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:01.209974image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:01.348982image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:01.490643image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:01.630275image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:01.769531image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:01.913825image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:02.055997image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:02.192957image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:02.326758image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:02.467384image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:02.603701image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:02.747890image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:02.888352image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:03.027580image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:03.166666image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:03.307338image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:03.448106image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:03.587425image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:03.726062image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:03.857834image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:03.997507image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:04.132503image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:04.275784image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:04.416728image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:04.555309image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:04.695338image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:04.838726image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:04.979425image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:05.118024image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:05.420563image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:05.549808image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:05.691933image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:05.821904image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:05.972061image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:06.103939image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:06.236544image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:06.371073image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:06.506322image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:06.641190image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:06.780338image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T00:28:10.029651image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T00:28:10.249846image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T00:28:10.471271image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T00:28:10.691703image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-25T00:28:07.032342image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:28:07.296720image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

oz1oz2oz3oz4oz5oz6oz7oz8oz9oz10target
0-0.4180470.2116350.9834681.4655240.0959590.4259460.1669820.256584-0.1878420.4014961.138993
1-0.281382-0.124388-0.431395-0.133318-0.6857801.9049471.1260971.116114-1.0863551.0765420.823558
2-0.602158-0.4000900.2298041.450013-0.171570-0.168297-0.536330-0.958720-0.4977960.4820561.103625
30.4137380.8631280.199243-1.1093750.0669401.5547780.3985620.6692111.2961651.215163-1.886359
4-0.024579-0.0226130.1813300.292579-0.1819870.4455191.432216-1.465030-0.7284530.1775020.582443
5-0.1301920.0804390.8717750.5590600.2357920.0581651.317248-1.6641271.445636-0.5976150.711213
61.0850780.6667700.3094540.3276760.537280-0.2786350.272891-0.677794-0.5463860.489837-1.811117
7-0.875658-0.702688-0.4124170.586572-0.3504001.9989691.3387161.276243-0.8963980.3781540.672476
8-1.441856-0.950143-0.912036-0.959146-1.094314-0.706765-1.657634-0.310944-0.299504-0.895168-0.612123
9-1.560233-1.418625-1.4037370.480123-0.9109642.1482161.1550821.554559-0.5062600.4078490.415005

Last rows

oz1oz2oz3oz4oz5oz6oz7oz8oz9oz10target
4901.5521900.9627260.501549-0.4498681.122378-0.0966891.109177-1.0231971.517592-0.452346-0.523032
491-1.073632-1.186437-1.095689-1.379145-0.762854-0.525192-0.301703-1.199937-1.0566560.670028-0.538339
492-0.650751-0.1040140.5486080.905587-0.342186-0.908074-0.283873-1.024488-0.925950-0.8060110.980483
493-0.679557-1.249957-1.6172370.110414-0.9094290.3697640.228934-0.6016511.0748650.0517010.759525
4940.8365251.427734-0.349674-0.895157-0.3366992.2977941.2490031.358112-0.3875790.852078-1.408836
4950.5591020.599597-0.661350-1.272904-0.0256440.4957180.5408330.314823-1.632903-0.018034-1.804161
4961.5896471.4061312.1884861.2017222.8588781.5690490.3686961.2953510.6102191.6000762.201056
4971.0355151.691008-1.042607-1.319776-0.512589-1.254787-0.185469-1.3468180.016264-1.2674340.082616
4980.3359590.361643-0.589462-0.347143-0.391881-0.376544-1.084070-0.1174210.5454711.134163-0.916912
499-0.201218-0.080412-1.263899-1.100934-0.6864730.8707430.8241940.0785760.1639180.9043100.888081